The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.
Data analysis is a process of inspecting, cleaning, transforming, and/or modeling data for objectives such as discovering useful information, suggesting conclusions, and/or supporting decision making. Various data analysis techniques involve repositories of data structures, such as data objects, stored on computer-readable media. An object is typically comprised of named substructures, which are often referred to as fields or properties. In many repositories, objects may be interrelated based on their properties and/or relationship data external to the objects. Two related objects are said to be associated, related, or linked.
Many repositories conform to one or more data models, ontologies, schemas, or other architectural frameworks, that place constraints on how objects are organized. For example, in many repositories, objects conform to one of a plurality of defined object types. Among other aspects, the type of an object specifies which types of substructures are found in the object. These substructures, referred to herein as properties, are typically assigned property names.
Some computer-based tools for analyzing data allow an analyst to visualize data objects in a variety of manners, or allow the analyst to mine, investigate, and/or take actions based upon the data objects. For instance, some types of data analysis tools allow an analyst to build graphs, charts, and/or reports based on various data objects. Another type of data analysis tool represents objects as linked nodes within a graph. Another type of data analysis tool involves workflows comprising a series of action nodes that input one set of data objects and output another set of data objects.
In the course of analyzing data, a computer, analyst, or other entity may determine that one or more actions need to be performed. For example, an analyst may determine that certain data objects are of interest for varying reasons, and that one or more actions need to be performed to investigate and/or address those data objects.
For instance, many organizations utilize data analysis processes to conduct operations that include organization members performing activities in a dispersed geographic area. The operations of a law enforcement agency typically include police officers patrolling assigned geographic areas, responding to crime scenes, and interviewing suspects and witnesses. Or, a disaster relief organization may respond to a natural disaster by sending out aid workers to a disaster area to locate and provide assistance to those in crisis. These types of operations may be referred to as field operations and may generally include monitoring specific geographic areas and subjects, interacting with persons of interest, responding to and reporting information about the occurrence of notable events, and any other activities that an organization member may perform in the field. In order to better coordinate field operations, an organization may employ one or more other organization members at a centralized location, referred to herein as operations analysts, that help coordinate the activities of the organization members in the field, referred to herein as field analysts. For example, operations analysts may be responsible for instructing field analysts on various actions or operations that need to be performed in view of a data analysis. Such actions or operations might include, for example, locations to investigate or subjects to monitor. Similarly, field analysts may be expected to communicate certain information related to the field operations back to operations analysts.